/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.cloudera.spark.randomforest;

import com.cloudera.spark.mllib.SparkConfUtil;
import scala.Tuple2;

import java.util.HashMap;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.tree.RandomForest;
import org.apache.spark.mllib.tree.model.RandomForestModel;
import org.apache.spark.mllib.util.MLUtils;

public final class JavaRandomForest {

    /**
     * Note: This example illustrates binary classification.
     * For information on multiclass classification, please refer to the JavaDecisionTree.java
     * example.
     */
    public static void testClassification(JavaRDD<LabeledPoint> trainingData,
                                           JavaRDD<LabeledPoint> testData) {
        // Train a RandomForest model.
        //  Empty categoricalFeaturesInfo indicates all features are continuous.
        Integer numClasses = 2;

        // storing arity of categorical features. E.g., an entry (n -> k) indicates that feature n is categorical with k categories indexed from 0: {0, 1, ..., k-1}
        HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();

        Integer numTrees = 3; // Use more in practice.
        String featureSubsetStrategy = "auto"; // Let the algorithm choose.
        String impurity = "gini";
        Integer maxDepth = 4;
        Integer maxBins = 32;
        Integer seed = 12345;

        final RandomForestModel model = RandomForest.trainClassifier(trainingData, numClasses,
                categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins,
                seed);

        // Evaluate model on test instances
        JavaPairRDD<Double, Double> predictionAndLabel =
                testData.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
                    @Override
                    public Tuple2<Double, Double> call(LabeledPoint p) {
                        return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
                    }
                });

        // compute test error
        Double testErr =
                1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() {
                    @Override
                    public Boolean call(Tuple2<Double, Double> pl) {
                        return !pl._1().equals(pl._2());
                    }
                }).count() / testData.count();
        System.out.println("Test Error: " + testErr);
        System.out.println("Learned classification forest model:\n" + model.toDebugString());
    }

    public static void testRegression(JavaRDD<LabeledPoint> trainingData,
                                       JavaRDD<LabeledPoint> testData) {
        // Train a RandomForest model.
        //  Empty categoricalFeaturesInfo indicates all features are continuous.
        HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
        Integer numTrees = 3; // Use more in practice.
        String featureSubsetStrategy = "auto"; // Let the algorithm choose.
        String impurity = "variance";
        Integer maxDepth = 4;
        Integer maxBins = 32;
        Integer seed = 12345;

        final RandomForestModel model = RandomForest.trainRegressor(trainingData,
                categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins,
                seed);

        // Evaluate model on test instances and compute test error
        JavaPairRDD<Double, Double> predictionAndLabel =
                testData.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
                    @Override
                    public Tuple2<Double, Double> call(LabeledPoint p) {
                        return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
                    }
                });
        Double testMSE =
                predictionAndLabel.map(new Function<Tuple2<Double, Double>, Double>() {
                    @Override
                    public Double call(Tuple2<Double, Double> pl) {
                        Double diff = pl._1() - pl._2();
                        return diff * diff;
                    }
                }).reduce(new Function2<Double, Double, Double>() {
                    @Override
                    public Double call(Double a, Double b) {
                        return a + b;
                    }
                }) / testData.count();
        System.out.println("Test Mean Squared Error: " + testMSE);
        System.out.println("Learned regression forest model:\n" + model.toDebugString());
    }

    public static void main(String[] args) {
        // spark context
        SparkConf sparkConf = new SparkConf().setAppName("JavaRandomForestExample");
        SparkConfUtil.setConf(sparkConf);
        JavaSparkContext sc = new JavaSparkContext(sparkConf);


        // Load and parse the data file.
        String datapath = "data/svm";
        JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc.sc(), datapath).toJavaRDD();

        // Split the data into training and test sets (30% held out for testing)
        JavaRDD<LabeledPoint>[] splits = data.randomSplit(new double[]{0.7, 0.3});
        JavaRDD<LabeledPoint> trainingData = splits[0];
        JavaRDD<LabeledPoint> testData = splits[1];

        // classification using RandomForest
        System.out.println("\nRunning example of classification using RandomForest\n");
        testClassification(trainingData, testData);

        // regression using RandomForest
        System.out.println("\nRunning example of regression using RandomForest\n");
        testRegression(trainingData, testData);

        sc.stop();
    }
}